{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": "### 柱形图绘制\n定义柱形图中的柱体的高低 度量\n柱体距离x轴的距离"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": "numcol \u003d [\u0027RT_user_norm\u0027, \u0027Metacritic_user_nom\u0027,  \u0027IMDB_norm\u0027, \u0027Fandango_Ratingvalue\u0027, \u0027Fandango_Stars\u0027]\nbar_height\u003d norm_reviews.ix[]\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "outputs": [
        {
          "data": {
            "text/plain": "\u003cFigure size 648x216 with 3 Axes\u003e",
            "image/png": 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\n"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 1 Axes\u003e",
            "image/png": 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\u003d\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "import matplotlib.pyplot as plt\n\ndata \u003d {\u0027apples\u0027: 10, \u0027oranges\u0027: 15, \u0027lemons\u0027: 5, \u0027limes\u0027: 20}\nnames \u003d list(data.keys())\nvalues \u003d list(data.values())\n\nfig, axs \u003d plt.subplots(1, 3, figsize\u003d(9, 3), sharey\u003dTrue)\naxs[0].bar(names, values)    # 直方图\naxs[1].scatter(names, values)  # 散点图\naxs[2].plot(names, values)    # 折线图\nfig.suptitle(\u0027Categorical Plottingggg\u0027) # 底层标题\ncat \u003d [\"bored\", \"happy\", \"bored\", \"bored\", \"happy\", \"bored\"]\ndog \u003d [\"happy\", \"happy\", \"happy\", \"happy\", \"bored\", \"bored\"]\nactivity \u003d [\"combing\", \"drinking\", \"feeding\", \"napping\", \"playing\", \"washing\"]\n\nfig, ax \u003d plt.subplots()  # 绘制底层图框\nax.plot(activity, dog, label\u003d\"dog\")\nax.plot(activity, cat, label\u003d\"cat\")\nax.legend() # 插入标签\n\nplt.show()\n\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%% \n",
          "is_executing": false
        }
      }
    }
  ],
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}